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Image super-resolution rebuilding method based on confidence coefficient kernel regression

A technology of super-resolution reconstruction and confidence, which is applied in the field of image processing, can solve the problems of unsatisfactory suppression and unsatisfactory results, and achieve the effect of increasing the reliability of pixels, suppressing outliers, and strong robustness

Inactive Publication Date: 2014-08-06
HOHAI UNIV
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Problems solved by technology

However, under abnormal conditions such as severe electromagnetic interference, registration error, fuzzy estimation error, occlusion, etc., the image contains some extremely bright or extremely dark pixels or a very small area composed of several pixels, the adaptive kernel regression is not effective in suppressing outliers. ideal
At this time, using adaptive kernel regression for image interpolation and participating in super-resolution reconstruction will naturally fail to achieve satisfactory results

Method used

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  • Image super-resolution rebuilding method based on confidence coefficient kernel regression
  • Image super-resolution rebuilding method based on confidence coefficient kernel regression
  • Image super-resolution rebuilding method based on confidence coefficient kernel regression

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Embodiment Construction

[0031] Below in conjunction with the accompanying drawings, a kind of image super-resolution reconstruction method based on confidence kernel regression proposed by the present invention is described in detail:

[0032] Such as figure 1 As shown, the specific implementation process of an image super-resolution reconstruction method based on confidence kernel regression is as follows:

[0033] ① Input N frames of low-resolution images.

[0034] The input low-resolution images are all images polluted by warping, blurring, downsampling and noise.

[0035] ②Using the first frame image as a reference frame, estimate the motion parameters of the remaining frames.

[0036] The motion estimation method used is the commonly used motion estimation based on the optical flow method.

[0037] ③According to the motion parameters, project N frames of images into a standard grid to obtain a non-uniform distribution image z(x i ); the cells of the standard grid are all square, and the reso...

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Abstract

The invention provides an image super-resolution rebuilding method based on the confidence coefficient kernel regression. According to the method, a confidence coefficient kernel function is constructed, the confidence coefficient kernel regression is carried out on an image through the confidence coefficient kernel function, interpolation in the image super resolution rebuilding is finished, by the adoption of the confidence coefficient kernel regression, not only is a double weighted mechanism of the self-adaption kernel regression inherited, but also distinguishing of pixel reliability is added, the abnormal value can be better restrained, and the image super-resolution rebuilding is achieved. According to the method, the image super-resolution rebuilding is achieved, the robustness is high, and the method can be widely applied to the fields such as monitoring, remote sensing, military, medical science and video entertainment and has the important value.

Description

technical field [0001] The invention belongs to the technical field of image processing, and specifically refers to an image super-resolution reconstruction method based on confidence kernel regression. Background technique [0002] With the advent of the information age, digital imaging technology and related applications have achieved rapid development. Many CCD (charge-coupled device) and CMOS image sensors are widely used to acquire digital images. Although these sensors can meet most imaging applications, their resolution level and cost are far from meeting people's consumption needs, especially the needs of high-end technology research and development. For example, in digital imaging applications such as monitoring, remote sensing, military, medical and video entertainment, high-quality images require not only sufficient pixel density but also rich detail information. Due to the influence of technological level, cost and other factors, it is often not realistic to re...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 徐枫陈哲徐立中严锡君石爱业
Owner HOHAI UNIV
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